فهرست مطالب hessam najafi

Systems for assessing groundwater vulnerability are designed to protect groundwater resources from pollution. The DRASTIC method is a well-known approach for determining groundwater susceptibility. One drawback of the DRASTIC method is that it relies on expert judgment to rank parameters, which introduces uncertainty. This study used a new generation of Fuzzy Logic (FL), called the Z-number theory, to estimate the specific vulnerability of aquifers and address this uncertainty. The specific vulnerability of the Ardabil and Qorveh-Dehgolan aquifers was estimated using two scenarios: the DRASTIC parameters as inputs and nitrate concentration values as output. The vulnerability of the aquifer was also evaluated by comparing the results of the proposed models with those of the DRASTIC model, which served as a benchmark. The analysis showed that the Z-number Based Modeling (ZBM), which considered data reliability and weighted the rules appropriately, produced higher-quality results than the classic FL. In the Ardabil plain, the ZBM yielded results that were 53% better (using seven inputs) and 184% better (using four inputs) compared to the classic FL. In the Qorveh-Dehgolan Plain (QDP), the ZBM produced results that were 127% better (using seven inputs) and 311% better (using four inputs) than the classic FL. The irregularity and non-linearity of the data, such as the high coefficient of variation (CV) in the Ardabil plain compared to the QDP, may contribute to the high CV value in the plains. Therefore, in plains with high CV, the quality of the extracted Z-number-based rules may be lower.

Doubtlessly hydroclimatic models play important role in the management of water resources. The hydroclimatic time series have three principle components (autoregressive, seasonality and stochastic) and the performance of the models related to these components, In the current research, the wavelet transform was linked to the Holt-Winters (HW) model for prediction of Lighvanchai, Trinity, West Nishnabotna watersheds monthly runoff and minimum temperature of Tabriz. The obtained results were compared with autoregressive and seasonal models such as ARIMA, seasonal ARIMA (SARIMA) and HW. For this purpose, the main time series were decomposed to some multi-frequency time series by wavelet transform. Then due to the univariated nature of the HW model, these subseries were imposed as input data to the HW models with two considered scenarios. In the first scenario only approximation subseries and one detail subseries (resulting from the accumulation of all details subseries) and in the second scenario all subseries were used as input to HW models .The obtained results show the second scenario of hybrid wavelet-holtwinters model (WHW2) could lead to considerably increased accuracy of both runoff and temperature monthly modeling because of multiscale analysis and considering all multi-frequency subseries.